Vers des écosystèmes d'objets sociaux

International audienceLe concept d'objet social est structurant pour le web à l'heure où le découplage entre plateformes de contenus et plateformes sociales s'accentue. Nous étudions dans cet article la notion d'objet social au travers d'un faisceau d'évolutions technologiques qui lui sont favorables. Premièrement les technologies du web sémantique et leur application au web social suscitent l'intérêt grandissant des acteurs du web. Deuxièmement l'avènement de la vision de l'informatique ubiquitaire réduit l'écart entre les sphères numériques et physiques et impacte les interactions sociales. Dans ce contexte porteur nous proposons une représentation du concept d'objet social et des structures de communications afférentes en vue d'une communication plus efficace. Ce travail débouche sur l'ontologie OCSO présentée dans cet article

Pervasive sociality: advanced social objects recommendation

International audienceThis paper makes a focus on social objects and stresses the importance of object-centered sociality in emerging technological context. Actual digital object centered sociality limits and related challenges are reviewed. We propose to foster social interactions through advanced social objects recommendations. In this context the Object Centered Sociality Ontology (OCSO) is presented. The paper concludes with research perspectives and a promising approach for social object recommendation through spreading activation algorithm

Discovery Hub: on-the-fly linked data exploratory search

International audienceExploratory search systems help users learn or investigate a topic. The richness of the linked open data can be used to assist this task. We present a method that selects and ranks linked data resources that are semantically related to the user's interest. The objective is to focus the user's attention on a meaningful subset of highly informative resources. We extended spreading activation to typed graphs and coupled it with a graph sampling technique. The results selection and ranking is performed on-the-fly and doesn't require pre-processing. This allows addressing remote SPARQL endpoints. We describe first implementation on top of DBpedia. It is used by the Discovery Hub exploratory search system to select interesting resources, to support faceted browsing of the results, to provide explanations and to offer redirections to third-party services. Results of a user evaluation conclude the article

Composite interests' exploration thanks to on-the-fly linked data spreading activation

International audienceExploratory search systems are built specifically to help the user in his cognitive consuming search tasks like learning or topic investigation. Some of these systems are built on the top of linked data and use semantics to provide cognitively-optimized search experiences. Thanks to their richness and to their connected nature linked data datasets can serve as a ground for advanced exploratory search. We propose to address the case of mixed interests' exploration in the form of composite queries (several unitary interests combined) e.g. exploring results and make discoveries related to both The Beatles and Ken Loach. The main contribution of this paper is the proposition of a novel method that processes linked-data for exploratory search purpose. It makes use of a semantic spreading activation algorithm coupled with a sampling technique. Its particularity is to not require any results preprocessing. Consequently this method offers a high level of flexibility for querying and allows, among others, the expression of composite interests' queries on remote linked data sources. This paper also details the analysis of the algorithm behavior over DBpedia and describes an implementation: the Discovery Hub application. It is an exploratory search engine that notably supports composite queries. Finally the results of a user evaluation are presented

Project Memory in Design

International audienceLearning from past projects allows designers to avoid past errors and solve problems. A number of methods define techniques to memorize lessons and experiences from projects. We present in this chapter an overview of these methods by emphasizing their main contributions and their critical points

Exploratory Search on the Top of DBpedia Chapters with the Discovery Hub Application

International audienceDiscovery Hub is an exploratory search engine that helps users explore topics of interests for learning and leisure purposes.It makes use of a semantic spreading activation algorithm coupled with a sampling technique so that it does not require a preprocessing step. 1 Linked data based exploratory search and recommendation Exploratory search [2] systems are designed to assist users during expensive cognitive consuming search tasks such as learning or topic investigation. Theyprovide a high level of assistance during the navigation in the results space and advanced results explanations. In the past few years several works showed the interest of using linked datadatasets, and especially DBpedia 1 , for resources discovery in recommender and exploratory search systems. For instance Seevl 2 is a band recommender helping the discovery of musical content and artists on Youtube thanks to a recommendation algorithm and a faceted browsing functionality. MORE 3 is a movie recommender in the form of a Facebook application that performs film recommendations thanks to DBpedia.Aemoo 4 is an exploratory search system that offers a filtered view on the DBpediagraph and gives explanations on the relations between the resources shown to the user.Yovisto 5 is a video platform offeringan exploratory search featurethat proposes a ranked list of related topic besides search results